Publications

  • C. Bennemann, M. W. Beinker, D. Egloff and M. Gauckler: Teraflops for Games and Derivative Pricing. Wilmott Magazine, Issue 36, July 2008, p. 50-54.

    Financial computing continuously demands higher computing performance, which can no longer be accomplished by simply increasing clock speed. Cluster and grid infrastructures grow, their cost of ownership explodes. On the other hand, the latest GPU (Graphics Processing Unit) boards show impressive performance metrics. This leads to the questions if and how one can harness this power to bring financial computing to the next level. We analyze the pricing of equity basket options with a local volatility model implemented on a GPU. Our performance gains are very impressive.

  • D. Egloff: Monte Carlo algorithms for optimal stopping and statistical learning. Annals of Applied Probability Volume 15, Number 2, 2005, p. 1396-1432.

    The paper provides the first complete convergence prove of the famous Longstaff-Schwartz algorithm in a fully distribution free approach, thereby extending the earlier work of Clément, Lamberton and Protter. Techniques from statistical learning are applied to estimate the convergence rate and the sample complexity.

  • D. Egloff, M. Kohler, and N. Todorovic: A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options. Annals of Applied Probability Volume 17, Number 4, 2007, p. 1138-1171

    This paper suggests an advanced Monte Carlo algorithm to price high-dimensional Bermudan options, which applies a data dependent model selection strategy to choose a proper set of basis functions in an optimal way.

  • D. Egloff, M. Leippold: Quantile estimation with adaptive importance sampling. Forthcoming Annals of Statistics 2009.

    We introduce new quantile estimators with adaptive importance sampling. The estimators are based on weighted samples which are neither independent nor identically distributed. Using a new law of iterated logarithm we prove the convergence also in the very general case where quantiles are not unique. We illustrate the superiority of our new algorithm with a concrete application from credit portfolio risk analysis.

  • D. Egloff, M. Leippold, and L. Wu: The term structure of variance swap rates and optimal investing in variance swaps. Forthcoming, Journal of Financial and Quantitative Analysis

    We study the optimal investment decision on the variance swap contract and the stock index. We find that two factors are needed to capture the term structure variation of the variance swap rates. Also the presence of variance swap contracts alters the investor’s optimal portfolio decision.

    This paper won the award from INQUIRE Europe, INQUIRE UK and Q-Group in 2007.

  • D. Egloff, M. Leippold: American options with stochastic stopping time constraints. Applied Mathematical Finance, Volume 16, Issue 3, 2009, p. 287 – 305

    This paper concerns the pricing of American options with stochastic stopping time constraints expressed in terms of the states of a Markov process. We apply a transformation to express the contract as a generalized barrier option. The valuation problem of the barrier option leads to a stochastic Cauchy-Dirichlet problem which we numerically solve with a suitable extension of the Longstaff-Schwartz algorithm.

  • D. Egloff, M. Leippold, and P. Vanini: A simple model for credit contagion. Journal of Banking and Finance, Volume 31, 2007, p. 2475-2492.

    We propose a simple model of credit contagion in which we include macro- and micro-structural interdependencies among the debtors within a credit portfolio. The microstructure captures interdependencies between debtors that go beyond their exposure to common factors, e.g., business or legal interdependencies. We show that even for diversified portfolios, moderate micro-structural interdependencies have a significant impact on the tails of the loss distribution. This impact increases dramatically for less diversified microstructures.

    The paper “A Simple Model of Credit Contagion” won the STOXX 2004 Gold

    Award of the annual meeting of the European Financial Management Association.

  • D. Egloff, M. Leippold, S. Jöhri, and C. Dalbert: Optimal importance sampling for credit portfolios with stochastic approximation. Unpublished manuscript.

    The paper applies adaptive importance sampling to calculate the loss distribution of a credit portfolio. It is a non-technical version. For a detailed analysis of the quantile estimator we refer to our annals paper “Quantile estimation with adaptive importance sampling”.

  • D. Egloff and M. Gauckler: The Meat and Bones of Message Passing. Unpublished manuscript.

    The paper describes the main idea behind Boost MPI, a C++ library which makes it easy to optimize the inter-process communication for parallel programs that transmit the same data structures many times. Boost MPI grew out of a project at Zurich Cantonal Bank where we implemented a large distributed Monte Carlo simulation to calculate the loss distribution of credit portfolios. It is now part of the Boost C++ libraries.